Search results for: network diagnostic tool

Authors: Alexander Goncharenko, Andrey Denisov, Sergey Alyamkin, Evgeny Terentev

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The neural network quantization is highly desired procedure to perform before running neural networks on mobile devices. Quantization without fine-tuning leads to accuracy drop of the model, whereas commonly used training with quantization is done on the full set of the labeled data and therefore is both time- and resource-consuming. Real life applications require simplification and acceleration of quantization procedure that will maintain accuracy of full-precision neural network, especially for modern mobile neural network architectures like Mobilenet-v1, MobileNet-v2 and MNAS. Here we present a method to significantly optimize training with quantization procedure by introducing the trained scale factors for discretization thresholds that are separate for each filter. Using the proposed technique, we quantize the modern mobile architectures of neural networks with the set of train data of only ∼ 10% of the total ImageNet 2012 sample. Such reduction of train dataset size and small number of trainable parameters allow to fine-tune the network for several hours while maintaining the high accuracy of quantized model (accuracy drop was less than 0.5%). Ready-for-use models and code are available in the GitHub repository.

Keywords: distillation, machine learning, neural networks, quantization

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Authors: Peri Harish Kumar, Rafique Umer Harvitkar

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Background: The emergency department mandates maximum efficacy in the utilization of the available resources. Non-urgent patient visits pose a serious concern to the treatment, patient triage, and resources available. Aims and Objectives: We conducted a retrospective case-control study of the emergency department patient list from October 2019 to November 2022. A total of 839 patients formed part of the study. Somatic complaints, vital signs, diagnostic test results, admission to the hospital, etc., were some of the criteria used for the categorization of patients. Results: The proportion of non-urgent visits varied from 7.2% to 43%, with a median of 21%. Somatic complaints were the least associated with further hospital admissions (n=28%), while diagnostic test results were the most significant indicator of further hospital admissions (n=74%). Effective triage helped minimize emergency department admissions by 36%. Conclusion: Our study shows that effective triaging, patient counselling, and round-the-clock consumable monitoring helped in the effective management of patients admitted and also significantly helped provide treatment to the patients most in need.

Keywords: urgent visits, non-urgent visits, traiging, emergency department admissions

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Authors: Arjun Prakash, Vinutha H., Karthik N.

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BACKGROUND: Carpal tunnel syndrome (CTS) is a common entrapment neuropathy with an estimated prevalence of 0.6 - 5.8% in the general adult population. It is caused by compression of the Median Nerve (MN) at the wrist as it passes through a narrow osteofibrous canal. Presently, the diagnosis is established by the clinical symptoms and physical examination and Nerve conduction study (NCS) is used to assess its severity. However, it is considered to be painful, time consuming and expensive, with a false-negative rate between 16 - 34%. Ultrasonography (USG) is now increasingly used as a diagnostic tool in CTS due to its non-invasive nature, increased accessibility and relatively low cost. Elastography is a newer modality in USG which helps to assess stiffness of tissues. However, there is limited available literature about its applications in peripheral nerves. OBJECTIVES: Our objectives were to measure the Cross-Sectional Area (CSA) and elasticity of MN at the carpal tunnel using Grey scale Ultrasonography (USG), Strain Elastography (SE) and Shear Wave Elastography (SWE). We also made an attempt to independently evaluate the role of Gray scale USG, SE and SWE in grading the severity of CTS, keeping NCS as the gold standard. MATERIALS AND METHODS: After approval from the Institutional Ethics Review Board, we conducted a comparative cross sectional study for a period of 18 months. The participants were divided into two groups. Group A consisted of 54 patients with clinically diagnosed CTS who underwent NCS, and Group B consisted of 50 controls without any clinical symptoms of CTS. All Ultrasound examinations were performed on SAMSUNG RS 80 EVO Ultrasound machine with 2 - 9 Mega Hertz linear probe. In both groups, CSA of the MN was measured on Grey scale USG, and its elasticity was measured at the carpal tunnel (in terms of Strain ratio and Shear Modulus). The variables were compared between both groups by using ‘Independent t test’, and subgroup analyses were performed using one-way analysis of variance. Receiver operating characteristic curves were used to evaluate the diagnostic performance of each variable. RESULTS: The mean CSA of the MN was 13.60 + 3.201 mm2 and 9.17 + 1.665 mm2 in Group A and Group B, respectively (p < 0.001). The mean SWE was 30.65 + 12.996 kPa and 17.33 + 2.919 kPa in Group A and Group B, respectively (p < 0.001), and the mean Strain ratio was 7.545 + 2.017 and 5.802 + 1.153 in Group A and Group B respectively (p < 0.001). CONCLUSION: The combined use of Gray scale USG, SE and SWE is extremely useful in grading the severity of CTS and can be used as a painless and cost-effective alternative to NCS. Early diagnosis and grading of CTS and effective treatment is essential to avoid permanent nerve damage and functional disability.

Keywords: carpal tunnel, ultrasound, elastography, nerve conduction study

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Authors: Hadjadj Abdechafik, Kious Mecheri, Ameur Aissa

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The objective of this study is to develop a process of treatment of the vibratory signals generated during a horizontal high speed milling process without applying any coolant in order to establish a monitoring system able to improve the machining performance. Thus, many tests were carried out on the horizontal high speed centre (PCI Météor 10), in given cutting conditions, by using a milling cutter with only one insert and measured its frontal wear from its new state that is considered as a reference state until a worn state that is considered as unsuitable for the tool to be used. The results obtained show that the first harmonic follow well the evolution of frontal wear, on another hand a wavelet transform is used for signal processing and is found to be useful for observing the evolution of the wavelet approximations through the cutting tool life. The power and the Root Mean Square (RMS) values of the wavelet transformed signal gave the best results and can be used for tool wear estimation. All this features can constitute the suitable indicators for an effective detection of tool wear and then used for the input parameters of an online monitoring system. Although we noted the remarkable influence of the machining cycle on the quality of measurements by the introduction of a bias on the signal, this phenomenon appears in particular in horizontal milling and in the majority of studies is ignored.

Keywords: flank wear, vibration, milling, signal processing, monitoring

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Authors: Aysun Sezer

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Magnetic Resonance Imaging (MRI) is one of the advanced diagnostic tools for evaluating shoulder pathologies. Proton Density (PD)-weighted MRI sequences prove highly effective in detecting edema. However, they are deficient in the anatomical identification of bones due to a trauma-induced decrease in signal-to-noise ratio and blur in the traumatized cortices. Computer-based diagnostic systems require precise segmentation, identification, and localization of anatomical regions in medical imagery. Deep learning-based object detection algorithms exhibit remarkable proficiency in real-time object identification and localization. In this study, the YOLOv8 model was employed to detect humeral head and scapular regions in 665 axial PD-weighted MR images. The YOLOv8 configuration achieved an overall success rate of 99.60% and 89.90% for detecting the humeral head and scapula, respectively, with an intersection over union (IoU) of 0.5. Our findings indicate a significant promise of employing YOLOv8-based detection for the humerus and scapula regions, particularly in the context of PD-weighted images affected by both noise and intensity inhomogeneity.

Keywords: YOLOv8, object detection, humerus, scapula, IRM

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Authors: Riyadh Alsultani, Ali Majdi

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It is necessary to develop a design methodology that includes the possibility of seismic events occurring in a region, the vulnerability of the civil hydraulic structure, and the effects of the occurrence hazard on society, environment, and economy in order to evaluate the flood and earthquake risks of coastal bridge networks. This paper presents a design approach for the assessment of the risk and sustainability of coastal bridge networks under time-variant flood-earthquake conditions. The social, environmental, and economic indicators of the network are used to measure its sustainability. These consist of anticipated loss, downtime, energy waste, and carbon dioxide emissions. The design process takes into account the possibility of happening in a set of flood and earthquake scenarios that represent the local seismic activity. Based on the performance of each bridge as determined by fragility assessments, network linkages are measured. The network's connections and bridges' damage statuses after an earthquake scenario determine the network's sustainability and danger. The sustainability measures' temporal volatility and the danger of structural degradation are both highlighted. The method is shown using a transportation network in Baghdad, Iraq.

Keywords: sustainability, Coastal bridge networks, flood-earthquake risk, structural design

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Authors: Eduarda Dutra De Souza, Gabriela Hammes, Marina Bouzon, Carlos M. Taboada Rodriguez

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The search for minimizing harmful impacts on the environment has become the focus of global society, affecting mainly how to manage organizations. Thus, companies have sought to transform their activities into environmentally friendly initiatives by applying green practices throughout their supply chains. In the logistics domain, the implementation of environmentally sound practices is still in its infancy in emerging countries such as Brazil. Given the need to reduce these environmental damages, this study aims to evaluate the performance of green logistics (GL) in the plastics industry sector in order to help to improve environmental performance within organizations and reduce the impact caused by their activities. The performance tool was based on theoretical research and the use of experts in the field. The Analytic Hierarchy Process (AHP) was used to prioritize green practices and assign weight to the indicators contained in the proposed tool. The tool also allows the co-production of a single indicator. The developed tool was applied in an industry of the plastic packaging sector. However, this tool may be applied in different industry sectors, and it is adaptable to different sizes of companies. Besides the contributions to the literature, this work also presents future paths of research in the field of green logistics.

Keywords: AHP, green logistics, green supply chain, performance evaluation

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Authors: Ishtiaq Wahid, Masood Ahmad, Nighat Ayub, Sajad Ali

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Lifetime of a wireless sensor network (WSN) is directly proportional to the energy consumption of its constituent nodes. Routing in wireless sensor network is very challenging due its inherit characteristics. In hierarchal routing the sensor filed is divided into clusters. The cluster-heads are selected from each cluster, which forms a hierarchy of nodes. The cluster-heads are used to transmit the data to the base station while other nodes perform the sensing task. In this way the lifetime of the network is increased. In this paper a comparative study of hierarchal routing protocols are conducted. The simulation is done in NS-2 for validation.

Keywords: WSN, cluster, routing, sensor networks

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Authors: Ali Nadi, Ali Edrissi

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Relief demand and transportation links availability is the essential information that is needed for every natural disaster operation. This information is not in hand once a disaster strikes. Relief demand and network condition has been evaluated based on prediction method in related works. Nevertheless, prediction seems to be over or under estimated due to uncertainties and may lead to a failure operation. Therefore, in this paper a stochastic programming model is proposed to evaluate real-time relief demand and network condition at the onset of a natural disaster. To address the time sensitivity of the emergency response, the proposed model uses reinforcement learning for optimization of the total relief assessment time. The proposed model is tested on a real size network problem. The simulation results indicate that the proposed model performs well in the case of collecting real-time information.

Keywords: disaster management, real-time demand, reinforcement learning, relief demand

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Authors: Muhammad R. Ahmed, Mohammed Aseeri

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Wireless Sensor Network (WSN) consists of low-cost and multi functional resources constrain nodes that communicate at short distances through wireless links. It is open media and underpinned by an application driven technology for information gathering and processing. It can be used for many different applications range from military implementation in the battlefield, environmental monitoring, health sector as well as emergency response of surveillance. With its nature and application scenario, security of WSN had drawn a great attention. It is known to be valuable to variety of attacks for the construction of nodes and distributed network infrastructure. In order to ensure its functionality especially in malicious environments, security mechanisms are essential. Malicious or internal attacker has gained prominence and poses the most challenging attacks to WSN. Many works have been done to secure WSN from internal attacks but most of it relay on either training data set or predefined threshold. Without a fixed security infrastructure a WSN needs to find the internal attacks is a challenge. In this paper we present an internal attack detection method based on maximum entropy model. The final experimental works showed that the proposed algorithm does work well at the designed level.

Keywords: internal attack, wireless sensor network, network security, entropy

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Authors: Jitendra Aswani

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In the first section of this study, impact of Global Financial Crisis (GFC) on the synchronization of fourteen Asian Stock Markets (ASM’s) of countries like Hong Kong, India, Thailand, Singapore, Taiwan, Pakistan, Bangladesh, South Korea, Malaysia, Indonesia, Japan, China, Philippines and Sri Lanka, has been analysed using the network science and its metrics like degree of node, clustering coefficient and network density. Then in the second section of this study by introducing the US stock market in existing network and developing a Minimum Spanning Tree (MST) spread of crisis from the US stock market to Asian Stock Markets (ASM) has been explained. Data used for this study is adjusted the closing price of these indices from 6th January, 2000 to 15th September, 2013 which further divided into three sub-periods: Pre, during and post-crisis. Using network analysis, it is found that Asian stock markets become more interdependent during the crisis than pre and post crisis, and also Hong Kong, India, South Korea and Japan are systemic important stock markets in the Asian region. Therefore, failure or shock to any of these systemic important stock markets can cause contagion to another stock market of this region. This study is useful for global investors’ in portfolio management especially during the crisis period and also for policy makers in formulating the financial regulation norms by knowing the connections between the stock markets and how the system of these stock markets changes in crisis period and after that.

Keywords: global financial crisis, Asian stock markets, network science, Kruskal algorithm

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Authors: Shelby Simpson, William Stanley, Namir Naba, Xiaodi Wang

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Both t-SNE and UMAP are brand new state of art tools to predominantly preserve the local structure that is to group neighboring data points together, which indeed provides a very informative visualization of heterogeneity in our data. In this research, we develop a t-SNE and UMAP base neural network image classification algorithm to embed the original dataset to a corresponding low dimensional dataset as a preprocessing step, then use this embedded database as input to our specially designed neural network classifier for image classification. We use the fashion MNIST data set, which is a labeled data set of images of clothing objects in our experiments. t-SNE and UMAP are used for dimensionality reduction of the data set and thus produce low dimensional embeddings. Furthermore, we use the embeddings from t-SNE and UMAP to feed into two neural networks. The accuracy of the models from the two neural networks is then compared to a dense neural network that does not use embedding as an input to show which model can classify the images of clothing objects more accurately.

Keywords: t-SNE, UMAP, fashion MNIST, neural networks

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Authors: Valentine Seymour

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Environmental volunteering organisations use questionnaires to explore the relationship between environmental volunteers and their health. To the author’s best knowledge, no one has explored volunteers’ health perception, which could be considered when designing a health impact measurement tool used to increase effective communication. This paper examines environmental volunteers' perceptions of health, knowledge which can be used to design a health impact measurement tool. This study uses focus group interviews, content analysis, and a general inductive approach to explore the health perceptions of volunteers who engage in environmental volunteering activities from the perspective of UK charity The Conservation Volunteers. Findings showed that volunteer groups presented were relatively similar in how they defined the term health, with their overall conceptual model closely resembling that of the World Health Organization 1948 definition. This suggests that future health impact measurement tools in the environmental volunteering sector could base their design around the World Health Organization’s definition.

Keywords: health perception, impact measurement, mental models, tool development

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Authors: Mohammed Affanuddin H. Siddique, Jayesh S. Shukla, Chetan B. Meshram

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The neural networks are one of the power tools of machine learning. After the invention of perceptron in early 1980's, the neural networks and its application have grown rapidly. Neural networks are a technique originally developed for pattern investigation. The structure of a neural network consists of neurons connected through synapse. Here, we have investigated the different algorithms and cost function reduction techniques for optimization of vertical axis wind turbine (VAWT) rotor blades. The aerodynamic force coefficients corresponding to the airfoils are stored in a database along with the airfoil coordinates. A forward propagation neural network is created with the input as aerodynamic coefficients and output as the airfoil co-ordinates. In the proposed algorithm, the hidden layer is incorporated into cost function having linear and non-linear error terms. In this article, it is observed that the ANNs (Artificial Neural Network) can be used for the VAWT’s optimization.

Keywords: VAWT, ANN, optimization, inverse design

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Authors: Babak Bahri, Fatemeh Yassaee Meybodi, Changiz Eslahchi

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In the pursuit of effective cancer therapies, the exploration of combinatorial drug regimens is crucial to leverage synergistic interactions between drugs, thereby improving treatment efficacy and overcoming drug resistance. However, identifying synergistic drug pairs poses challenges due to the vast combinatorial space and limitations of experimental approaches. This study introduces ClusterSyn, a machine learning (ML)-powered framework for classifying anti-cancer drug synergy scores. ClusterSyn employs a two-step approach involving drug clustering and synergy score prediction using a fully connected deep neural network. For each cell line in the training dataset, a drug graph is constructed, with nodes representing drugs and edge weights denoting synergy scores between drug pairs. Drugs are clustered using the Markov clustering (MCL) algorithm, and vectors representing the similarity of drug pairs to each cluster are input into the deep neural network for synergy score prediction (synergy or antagonism). Clustering results demonstrate effective grouping of drugs based on synergy scores, aligning similar synergy profiles. Subsequently, neural network predictions and synergy scores of the two drugs on others within their clusters are used to predict the synergy score of the considered drug pair. This approach facilitates comparative analysis with clustering and regression-based methods, revealing the superior performance of ClusterSyn over state-of-the-art methods like DeepSynergy and DeepDDS on diverse datasets such as Oniel and Almanac. The results highlight the remarkable potential of ClusterSyn as a versatile tool for predicting anti-cancer drug synergy scores.

Keywords: drug synergy, clustering, prediction, machine learning., deep learning

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Authors: Saidi Nabiha, Khaled Zaatouri, Walid Fajraoui, Tahar Ezzeddine

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The market of Wireless Sensor Network WSN has a great potential and development opportunities. Researchers are focusing on optimization in many fields like efficient deployment and routing protocols. In this article, we will concentrate on energy efficiency for WSN because WSN nodes are habitually deployed in severe No Man’s Land with batteries are not rechargeable, so reducing energy consumption represents an important challenge to extend the life of the network. We will present the design of new WSN mote based on ultra low power STM32L microcontrollers and the ZIGBEE transceiver CC2520. We will compare it to existent motes and we will conclude that our mote is promising in energy consumption.

Keywords: component, WSN mote, power consumption, STM32L, sensors, CC2520

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Authors: Marta Skwarecka, Patrycja Bloch, Rafal Walkusz, Oliwia Urbanowicz, Grzegorz Zielinski, Sabina Zoledowska, Dawid Nidzworski

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Upper respiratory tract infections are one of the most common reasons for visiting a general doctor. Streptococci are the most common bacterial etiological factors in these infections. There are many different types of Streptococci and infections vary in severity from mild throat infections to pneumonia. For example, S. pyogenes mainly contributes to acute pharyngitis, palatine tonsils and scarlet fever, whereas S. Streptococcus pneumoniae is responsible for several invasive diseases like sepsis, meningitis or pneumonia with high mortality and dangerous complications. There are only a few diagnostic tests designed for detection Streptococci from the infected throat of patients. However, they are mostly based on lateral flow techniques, and they are not used as a standard due to their low sensitivity. The diagnostic standard is to culture patients throat swab on semi selective media in order to multiply pure etiological agent of infection and subsequently to perform antibiogram, which takes several days from the patients visit in the clinic. Therefore, the aim of our studies is to develop and implement to the market a Point of Care device for the rapid identification of Streptococcus pyogenes and Streptococcus pneumoniae with simultaneous identification of antibiotic resistance genes. In the course of our research, we successfully selected genes for to-species identification of Streptococci and genes encoding antibiotic resistance proteins. We have developed a reaction to amplify these genes, which allows detecting the presence of S. pyogenes or S. pneumoniae followed by testing their resistance to erythromycin, chloramphenicol and tetracycline. What is more, the detection of β-lactamase-encoding genes that could protect Streptococci against antibiotics from the ampicillin group, which are widely used in the treatment of this type of infection is also developed. The test is carried out directly from the patients' swab, and the results are available after 20 to 30 minutes after sample subjection, which could be performed during the medical visit.

Keywords: antibiotic resistance, Streptococci, respiratory infections, diagnostic test

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Authors: Rawaa H. El-Bidweihy, Hisham M. Abdelsalam, Ihab A. El-Khodary

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In an era where machines run and shape our world, the need for a stable, non-ending source of energy emerges. In this study, the focus was on the solar energy in Egypt as a renewable source, the most important factors that could affect the solar energy’s market share throughout its life cycle production were analyzed and filtered, the relationships between them were derived before structuring a Bayesian network. Also, forecasted models were built for multiple factors to predict the states in Egypt by 2035, based on historical data and patterns, to be used as the nodes’ states in the network. 37 factors were found to might have an impact on the use of solar energy and then were deducted to 12 factors that were chosen to be the most effective to the solar energy’s life cycle in Egypt, based on surveying experts and data analysis, some of the factors were found to be recurring in multiple stages. The presented Bayesian network could be used later for scenario and decision analysis of using solar energy in Egypt, as a stable renewable source for generating any type of energy needed.

Keywords: ARIMA, auto correlation, Bayesian network, forecasting models, life cycle, partial correlation, renewable energy, SARIMA, solar energy

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Authors: Ehsan Yadegari

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While there are several metaheuristics and exact approaches to solving the Supply Chain Network Design (SCND) problem, there still remains an unfilled gap in using the Teaching-Learning-Based Optimization (TLBO) algorithm. The algorithm has demonstrated desirable results with problems with complicated combinational optimization. The present study introduces a Discrete Self-Study TLBO (DSS-TLBO) with priority-based solution representation that can solve a supply chain network configuration model to lower the total expenses of establishing facilities and the flow of materials. The network features four layers, namely suppliers, plants, distribution centers (DCs), and customer zones. It is designed to meet the customer’s demand through transporting the material between layers of network and providing facilities in the best economic Potential locations. To have a higher quality of the solution and increase the speed of TLBO, a distinct operator was introduced that ensures self-adaptation (self-study) in the algorithm based on the four types of local search. In addition, while TLBO is used in continuous solution representation and priority-based solution representation is discrete, a few modifications were added to the algorithm to remove the solutions that are infeasible. As shown by the results of experiments, the superiority of DSS-TLBO compared to pure TLBO, genetic algorithm (GA) and firefly Algorithm (FA) was established.

Keywords: supply chain network design, teaching–learning-based optimization, improved metaheuristics, discrete solution representation

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Authors: Paulu Saramge Shalika Nirupani Seram

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The study delves into the complex dynamics of social networks and how they affect paddy farmers’ adoption of agricultural technologies, which are included in Yaya Development program, Weedy rice program and Good Agricultural Practices (GAP) program in Matara district. Identify the social networks among the farmers of ongoing Extension Programs in Matara district, examine the farmers’ adoption level to the ongoing extension programs in Matara district, analyze the impacts of social networks for the adoption to the technologies of ongoing extension programs and give suggestions and recommendations to improve the social network of paddy farmers in Matara District for ongoing extension programs are the objectives of this research. A structured questionnaire survey was conducted with 25 farmers from Matara-North (Wilpita), 25 farmers from Matara-Central (Kamburupitiya), and 25 farmers from Matara-South (Malimbada). UCINET (Version -6.771) software was used for social network analysis, and other than that, descriptive statistics and inferential statistics were used to analyze the findings. Matara-North has the highest social network density, and Matara-South has the lowest social network density according to the social network analysis. Dissemination of intensive technologies requires the most prominent actors of the social network, and in Matara district, agricultural instructors have the highest ability to disseminate technologies. The influence of actors in the social network, the trustworthiness of AI officers, and the trust of indigenous knowledge about paddy cultivation have a significant effect on the technology adoption of farmers. The research endeavors to contribute a nuanced understanding of the social networks and agricultural technology adoption in Matara District, offering practical insights for stakeholders involved in agricultural extension services.

Keywords: agricultural extension, paddy cultivation, social network, technology adoption

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Authors: Lee Yan Nian

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Safety investigation is different from an administrative investigation in that the former is conducted by an independent agency and the purpose of such investigation is to prevent accidents in the future and not to apportion blame or determine liability. Before October 2018, Taiwan railway occurrences were investigated by local supervisory authority. Characteristics of this kind of investigation are that enforcement actions, such as administrative penalty, are usually imposed on those persons or units involved in occurrence. On October 21, 2018, due to a Taiwan Railway accident, which caused 18 fatalities and injured another 267, establishing an agency to independently investigate this catastrophic railway accident was quickly decided. The Taiwan Transportation Safety Board (TTSB) was then established on August 1, 2019 to take charge of investigating major aviation, marine, railway and highway occurrences. The objective of this study is to assess the effectiveness of safety investigations conducted by the TTSB. In this study, the major railway occurrence investigation reports published by the TTSB are used for modeling and analysis. According to the classification of railway occurrences investigated by the TTSB, accident types of Taiwan railway occurrences can be categorized into: derailment, fire, Signal Passed at Danger and others. A Causal Factor Analysis System (CFAS) developed by the TTSB is used to identify the influencing causal factors and their causal relationships in the investigation reports. All terminologies used in the CFAS are equivalent to the Human Factors Analysis and Classification System (HFACS) terminologies, except for “Technical Events” which was added to classify causal factors resulting from mechanical failure. Accordingly, the Bayesian network structure of each occurrence category is established based on the identified causal factors in the CFAS. In the Bayesian networks, the prior probabilities of identified causal factors are obtained from the number of times in the investigation reports. Conditional Probability Table of each parent node is determined from domain experts’ experience and judgement. The resulting networks are quantitatively assessed under different scenarios to evaluate their forward predictions and backward diagnostic capabilities. Finally, the established Bayesian network of derailment is assessed using investigation reports of the same accident which was investigated by the TTSB and the local supervisory authority respectively. Based on the assessment results, findings of the administrative investigation is more closely tied to errors of front line personnel than to organizational related factors. Safety investigation can identify not only unsafe acts of individual but also in-depth causal factors of organizational influences. The results show that the proposed methodology can identify differences between safety investigation and administrative investigation. Therefore, effective intervention strategies in associated areas can be better addressed for safety improvement and future accident prevention through safety investigation.

Keywords: administrative investigation, bayesian network, causal factor analysis system, safety investigation

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Authors: Svenja Pieritz, Jakab Pilaszanovich

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Collaborative Problem Solving (CPS) is recognized as being one of the most important skills of the 21st century with having a potential impact on education, job selection, and collaborative systems design. Therefore, CPS has been adopted in several standardized tests, including the Programme for International Student Assessment (PISA) in 2015. A significant challenge of evaluating CPS is the underlying interplay of cognitive and social skills, which requires a more holistic assessment. However, the majority of the existing tests are using a questionnaire-based assessment, which oversimplifies this interplay and undermines ecological validity. Two major difficulties were identified: Firstly, the creation of a controllable, real-time environment allowing natural behaviors and communication between at least two people. Secondly, the development of an appropriate method to collect and synthesize both cognitive and social metrics of collaboration. This paper proposes a more holistic and automated approach to the assessment of CPS. To address these two difficulties, a multiplayer problem-solving game called Sea of Light was developed: An environment allowing students to deploy a variety of measurable collaborative strategies. This controlled environment enables researchers to monitor behavior through the analysis of game actions and chat. The according solution for the statistical model is a combined approach of Natural Language Processing (NLP) and Bayesian network analysis. Social exchanges via the in-game chat are analyzed through NLP and fed into the Bayesian network along with other game actions. This Bayesian network synthesizes evidence to track and update different subdimensions of CPS. Major findings focus on the correlations between the evidences collected through in- game actions, the participants’ chat features and the CPS self- evaluation metrics. These results give an indication of which game mechanics can best describe CPS evaluation. Overall, Sea of Light gives test administrators control over different problem-solving scenarios and difficulties while keeping the student engaged. It enables a more complete assessment based on complex, socio-cognitive information on actions and communication. This tool permits further investigations of the effects of group constellations and personality in collaborative problem-solving.

Keywords: bayesian network, collaborative problem solving, game-based assessment, natural language processing

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Authors: Myung-Gon Yoon, Jung-Ho Moon, Tae Kwon Ha

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This paper considers a robust stabilization problem of a single agent in a multi-agent consensus system composed of identical agents, when the network topology of the system is completely unknown. It is shown that the transfer function of an agent in a consensus system can be described as a multiplicative perturbation of the isolated agent transfer function in frequency domain. Applying known robust stabilization results, we present sufficient conditions for a robust stabilization of an agent against unknown network topology.

Keywords: single agent control, multi-agent system, transfer function, graph angle

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Authors: Chia-Feng Lu, Yuh-Jen Wang, Shin Teng, Yu-Te Wu, Sui-Hing Yan

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Brain functional networks based on resting-state EEG data were compared between patients with mild Alzheimer’s disease (mAD) and matched patients with amnestic subtype of mild cognitive impairment (aMCI). We integrated the time–frequency cross mutual information (TFCMI) method to estimate the EEG functional connectivity between cortical regions and the network analysis based on graph theory to further investigate the alterations of functional networks in mAD compared with aMCI group. We aimed at investigating the changes of network integrity, local clustering, information processing efficiency, and fault tolerance in mAD brain networks for different frequency bands based on several topological properties, including degree, strength, clustering coefficient, shortest path length, and efficiency. Results showed that the disruptions of network integrity and reductions of network efficiency in mAD characterized by lower degree, decreased clustering coefficient, higher shortest path length, and reduced global and local efficiencies in the delta, theta, beta2, and gamma bands were evident. The significant changes in network organization can be used in assisting discrimination of mAD from aMCI in clinical.

Keywords: EEG, functional connectivity, graph theory, TFCMI

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Authors: Zaouche Khelil, Talha Abdelaziz, Berkouk El Madjid

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In this paper, we present a grid-tied photovoltaic system. The studied topology is structured around a seven-level inverter, supplying a non-linear load. A three-stage step-up DC/DC converter ensures DC-link balancing. The presented system allows the extraction of all the available photovoltaic power. This extracted energy feeds the local load; the surplus energy is injected into the electrical network. During poor weather conditions, where the photovoltaic panels cannot meet the energy needs of the load, the missing power is supplied by the electrical network. At the common connexion point, the network current shows excellent spectral performances.

Keywords: seven-level inverter, multi-level DC/DC converter, photovoltaic, non-linear load

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Authors: Christopher B. Obida, George A. Blackburn, James D. Whyatt, Kirk T. Semple

Abstract:

In many regions of the world, especially in developing countries, river network data are outdated or completely absent, yet such information is critical for supporting important functions such as flood mitigation efforts, land use and transportation planning, and the management of water resources. In this study, a method was developed for delineating river networks using Sentinel 1 imagery. Unsupervised classification was applied to multi-temporal Sentinel 1 data to discriminate water bodies from other land covers then the outputs were combined to generate a single persistent water bodies product. A thinning algorithm was then used to delineate river centre lines, which were converted into vector features and built into a topologically structured geometric network. The complex river system of the Niger Delta was used to compare the performance of the Sentinel-based method against alternative freely available water body products from United States Geological Survey, European Space Agency and OpenStreetMap and a river network derived from a Shuttle Rader Topography Mission Digital Elevation Model. From both raster-based and vector-based accuracy assessments, it was found that the Sentinel-based river network products were superior to the comparator data sets by a substantial margin. The geometric river network that was constructed permitted a flow routing analysis which is important for a variety of environmental management and planning applications. The extracted network will potentially be applied for modelling dispersion of hydrocarbon pollutants in Ogoniland, a part of the Niger Delta. The approach developed in this study holds considerable potential for generating up to date, detailed river network data for the many countries where such data are deficient.

Keywords: Sentinel 1, image processing, river delineation, large scale mapping, data comparison, geometric network

Procedia PDF Downloads 139

Authors: Azli Bin Ariffin, Noor Amy Afiza Binti Mohd Yusof

Abstract:

This study aims to identify effect of social network usage on the self-learning method in teaching and learning at Sultan Idris Education University. The study involved 270 respondents consisting of students in the pre-graduate and post-graduate levels from nine fields of study offered. Assessment instrument used is questionnaire which measures respondent’s background includes level of study, years of study and field of study. Also measured the extent to which social pages used for self-learning and effect received when using social network for self-learning in learning process. The results of the study showed that students always visit Facebook more than other social sites. But, it is not for the purpose of self-learning. Analyzed data showed that 45.5% students not sure about using social sites for self-learning. But they realize the positive effect that they will received when use social sites for self-learning to improve teaching and learning process when 72.7% respondent agreed with all the statements provided.

Keywords: facebook, self-learning, social network, teaching, learning

Procedia PDF Downloads 537

Authors: Zahra Hatami, Hesham Ali, David Volkman

Abstract:

Selecting a portfolio for investing is a crucial decision for individuals and legal entities. In the last two decades, with economic globalization, a stream of financial innovations has rushed to the aid of financial institutions. The importance of selecting stocks for the portfolio is always a challenging task for investors. This study aims to create a financial network to identify optimal portfolios using network centralities metrics. This research presents a community detection technique of superior stocks that can be described as an optimal stock portfolio to be used by investors. By using the advantages of a network and its property in extracted communities, a group of stocks was selected for each of the various time periods. The performance of the optimal portfolios compared to the famous index. Their Sharpe ratio was calculated in a timely manner to evaluate their profit for making decisions. The analysis shows that the selected potential portfolio from stocks with low centrality measurement can outperform the market; however, they have a lower Sharpe ratio than stocks with high centrality scores. In other words, stocks with low centralities could outperform the S&P500 yet have a lower Sharpe ratio than high central stocks.

Keywords: portfolio management performance, network analysis, centrality measurements, Sharpe ratio

Procedia PDF Downloads 154

Authors: Malik Muhammad Arslan, Muneeb Ullah, Dai Shihan, Faizan Khan, Xiaodong Yang

Abstract:

Internet Service Providers are driving endless demands for higher bandwidth and data throughput as new services and applications require higher bandwidth. Users want immediate and accurate data delivery. This article focuses on converting old conventional networks into passive optical networks based on time division and wavelength division multiplexing. The main focus of this research is to use a hybrid of time-division multiplexing and wavelength-division multiplexing to improve network efficiency and performance. In this paper, we design an 80 Gbps Passive Optical Network (PON), which meets the need of the Next Generation PON Stage 2 (NGPON2) proposed in this paper. The hybrid of the Time and Wavelength division multiplexing (TWDM) is said to be the best solution for the implementation of NGPON2, according to Full-Service Access Network (FSAN). To co-exist with or replace the current PON technologies, many wavelengths of the TWDM can be implemented simultaneously. By utilizing 8 pairs of wavelengths that are multiplexed and then transmitted over optical fiber for 40 Kms and on the receiving side, they are distributed among 256 users, which shows that the solution is reliable for implementation with an acceptable data rate. From the results, it can be concluded that the overall performance, Quality Factor, and bandwidth of the network are increased, and the Bit Error rate is minimized by the integration of this approach.

Keywords: bit error rate, fiber to the home, passive optical network, time and wavelength division multiplexing

Procedia PDF Downloads 70

Authors: Dimitra Alexiou

Abstract:

In an urban area the allocation placement of an emergency service mobile units, such as ambulances, police patrol must be designed so as to achieve a prompt response to demand locations. In this paper, a partition of a given urban network into distinct sub-networks is performed such that; the vertices in each component are close and simultaneously the difference of the sums of the corresponding population in the sub-networks is almost uniform. The objective here is to position appropriately in each sub-network a mobile emergency unit in order to reduce the response time to the demands. A mathematical model in the framework of graph theory is developed. In order to clarify the corresponding method a relevant numerical example is presented on a small network.

Keywords: graph partition, emergency service, distances, location

Procedia PDF Downloads 499